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Learning to Resize Images for Computer Vision Tasks

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This document discusses a proposed method for learning optimal image resizing for computer vision tasks using deep learning. The method trains a resizing model using skip connections that can resize images for classification and quality estimation tasks while preserving important features, achieving better accuracy than conventional resizing methods. Experiments on ImageNet and AVA datasets show the proposed resizer lowers error rates compared to bilinear and bicubic resizing, and has lower computational cost than super-resolution models.

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Learning to Resize Images for Computer Vision Tasks Hossein Talebi, and Peyman Milanfar Google Research 1 2021/05/13 平間 友大
論文概要 目的 ・CNNを用いたタスクにおいて，画像のリサイズと解像度に関する研究・ 調査は殆ど行われていないが，精度に大きく貢献すると思われる．そこで， 新たなリサイザーモデルを提案する． 手法 ・スキップ接続を用いた最適化リサイザーモデルを提案 結果 ・従来モデルと比較し，分類 と画像品質推定タスクで精度向上 論文URL https://arxiv.org/pdf/2103.09950v1.pdf 2021．05．13時点 2 実験結果
従来の画像リサイズ手法 3 バイリニア法：周辺2x2画素の 輝度値を直線的に補完 ・当たり前のように使っているが，これでいいのか？ ・機械学習を用いた画像認識タスクでは，精度や学習コストに対する最適な解 像度が存在するはずであり，従来手法自体はそれらを学習することはできない． https://imagingsolution.blog.fc2.com/blog-entry-142.html バイキュービック法：周辺4x4画素の 輝度値を3次元で補完 → 計算時間が増えるが多くの場合 滑らかにリサイズできる
4 提案手法の概要 提案手法の流れ タスクにおいて最適なリサイズを学習するリサイザーモデルを提案 ・既存の認識モデルやフレームワークに利用可能 ・超解像モデルよりも学習コストを抑えパフォーマンス向上を狙う
5 提案手法の概要 提案手法の流れ リサイザーモデルの出力結果 リサイザーモデルは認識モデルのロスを最小にするような画像を出力する 人が見やすいようにリサイズすることを目的としていない
リサイザーモデルの概要 • 従来のリサイズ手法と，Res block，スキップ接続の組み合わせ • 分類に重要なオリジナル画像の特徴を保持しつつ，リサイズすることを 狙う 6 x1000 パラメータ数
実験設定 7 2つの損失関数で比較 画像のクオリティ評価（IQA） Earth Mover‘s Distance（EMD）回帰損失（2）を用いて，リサイズされた画像が 品質の評価に与える影響を評価する 2つの分布の距離を測る距離尺度 クロスエントロピー関数 CDF：累積分布関数（確率変数Xがある値x以下（X≦x）となる確率を表す関数） クオリティラベルは10段階で複数人の評価のヒストグラム 𝑝：予測ラベル 𝑞：正解ラベル 𝐾: ラベル総 𝛿𝑘, 𝑦:正解時1，不正解0 𝜀：0.1で固定 𝑑：2で固定
実験設定 8 データセット ・ ImageNet dataset (1000クラス)： 飛行機や車，動物などの画像 ・AVA dataset[23]（10クラス）: 美的嗜好分析のためのデータセット 1-10の評価スコアのヒストグラムラベル 評価指標 ・ピアソンの線形相関係数（PLCC）： 予測された品質スコアとグラウンドトゥルース品質スコアの線形相関係数（1が最も良い） ・スピアマンの順位相関係数（SRCC） 正規分布に従わない分布間の相関関係を表す．（-1or1が最も良い） 学習環境 ・Tensorflow ・NVIDIA V100 GPU x4 事前学習に用いるリサイズはバイリニア法とバイキュービック法 [23] Naila Murray, Luca Marchesotti, and Florent Perronnin. Ava: A large-scale database for aesthetic visual analysis. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2408–2415. IEEE, 2012 https://www.researchgate.net/figure/Sample-images-from-the-Aesthetic- Visual-Analysis-AVA-database-sorted-by-their-aesthetic_fig1_309327222
実 験 結 果 9
実験結果 10 各リサイズ手法適用後の画像例
実 験 結 果 11
実験結果 12 上段：リサイザーの処理後の画像 下段：バイキュービック法との差分 分類に特徴のある部分のみ大きく変化している →リサイザーモデルが特徴を学習していることを示唆している
実験結果 13
実験結果 14 アブレーションスタディ
まとめ 15 • タスクごとに対応可能なリサイズと解像度を学習するリサイザーモデル を提案 ・従来手法と比較し，Error率を低下させた • 超解像モデルを使用するよりも学習コストが優れている • 従来のリサイズ手法よりも多くの場合処理が早い 個人的な見解 • これまで当たり前のようにやってきたリサイズ処理の最適化という部分 への着目が面白かった • 既存のCNN構造にリサイザーを挟むだけなので便利だが，高解像度に対 応したCNNを使ったほうが精度は高くないか？ →運用時の処理速度が早いのはメリットかも • リサイザーに入力するときにリサイズしているっぽい．．．？ • 入力サイズに柔軟（可変）であることや，画像の拡大への応用も検証し てほしい

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